Machine Learning for Everybody - Full Course
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- Опубликовано: 1 май 2024
- Learn Machine Learning in a way that is accessible to absolute beginners. You will learn the basics of Machine Learning and how to use TensorFlow to implement many different concepts.
✏️ Kylie Ying developed this course. Check out her channel: / ycubed
⭐️ Code and Resources ⭐️
🔗 Supervised learning (classification/MAGIC): colab.research.google.com/dri...
🔗 Supervised learning (regression/bikes): colab.research.google.com/dri...
🔗 Unsupervised learning (seeds): colab.research.google.com/dri...
🔗 Dataets (add a note that for the bikes dataset, they may have to open the downloaded csv file and remove special characters)
🔗 MAGIC dataset: archive.ics.uci.edu/ml/datase...
🔗 Bikes dataset: archive.ics.uci.edu/ml/datase...
🔗 Seeds/wheat dataset: archive.ics.uci.edu/ml/datase...
🏗 Google provided a grant to make this course possible.
⭐️ Contents ⭐️
⌨️ (0:00:00) Intro
⌨️ (0:00:58) Data/Colab Intro
⌨️ (0:08:45) Intro to Machine Learning
⌨️ (0:12:26) Features
⌨️ (0:17:23) Classification/Regression
⌨️ (0:19:57) Training Model
⌨️ (0:30:57) Preparing Data
⌨️ (0:44:43) K-Nearest Neighbors
⌨️ (0:52:42) KNN Implementation
⌨️ (1:08:43) Naive Bayes
⌨️ (1:17:30) Naive Bayes Implementation
⌨️ (1:19:22) Logistic Regression
⌨️ (1:27:56) Log Regression Implementation
⌨️ (1:29:13) Support Vector Machine
⌨️ (1:37:54) SVM Implementation
⌨️ (1:39:44) Neural Networks
⌨️ (1:47:57) Tensorflow
⌨️ (1:49:50) Classification NN using Tensorflow
⌨️ (2:10:12) Linear Regression
⌨️ (2:34:54) Lin Regression Implementation
⌨️ (2:57:44) Lin Regression using a Neuron
⌨️ (3:00:15) Regression NN using Tensorflow
⌨️ (3:13:13) K-Means Clustering
⌨️ (3:23:46) Principal Component Analysis
⌨️ (3:33:54) K-Means and PCA Implementations
🎉 Thanks to our Champion and Sponsor supporters:
👾 Raymond Odero
👾 Agustín Kussrow
👾 aldo ferretti
👾 Otis Morgan
👾 DeezMaster
--
Learn to code for free and get a developer job: www.freecodecamp.org
Read hundreds of articles on programming: freecodecamp.org/news
I have no idea how my RUclips algorithm brought me here while I was sleeping but it made for some strange dreams
I fell asleep once and ends up on this channel with a video about how to make a neural network playing.
There was also a time when I fell asleep watching a video about nihilism on repeat and a character in my dream said what the video was saying word for word. It freaked me out.
@Keys for Wealth like what? I’m not curious
Same here haha. I woke up with this vid.
I once fell asleep watching a space and time tv shows , in the dream I could understand what the guy was explaining in the docu , it’s crazy how the real speech of a person is translated in a dream .
@@fullmetaltheorist like same I am not doing a joke
I've been trying to learn ML for quite awhile but could never really grasp the algorithim. She explains how the formula comes about and why is it used in the classification or regression so well. My god. Thumbs up for sensei Kylie and free code camp!!!
Does it includes stats too? Or just ml algorithms?
@@networkserpent5155 im not quite sure what you mean. ml is mostly stats. I guess both would be the right answer.
Yeah same I'm always interested in learning where the algorithms and concepts we use in machine learning comes from and she explained it the most clearly to me. Thank you!
what programming language she used in this course?
@@yehiaelhariry9356 python
*NichesPanel likes this xD* we all know that they isn't, but do you think models buy followers to appear on the internet?
😊
spectrum
B😊
For anyone getting an error related to converting a list to a float, the model.evaluate is actually returning a list. She has the correction in the code at around 2:05:51, but she doesn't explicitly mention the correction. You just grab the first value in the list (which is why she puts [0]). So change the line where you obtain the val_loss to:
val_loss = model.evaluate(X_valid, y_valid)[0]
literal life saver man. Thank you
Thank you so much! You are truly a life saver.
i dont want to change the likes on here bc you killed it! 187!
Happy to help, everyone. It caught me a little off guard at first, and by the way she was using it I knew it had to be a collection of some sort and it turned out it was some little change she made and forgot to mention. No biggie, but it can definitely cause a headache - I know :-)
You just saved me!! I wrote literally the exact same question before reading you input, and then saw your query and ...voila!!! thanks
falling asleep lands me in odd places
I also fell asleep 😂😅
Bruh 4th time now
hahaha man same here
Kylie Ying is a gift to humanity
The way she explained bayes theorem
I generally donot find such clear explaination anywhere
It was reallized and practical explaination . Truly appreciative
Yesterday I click on a video called 'learning phyton for Beginners'. Today youtube's algorithm sent this video. I was so confuse but somehow listen to it and when I feel I understand something from this explanation, it makes me excited. A genius can make someone understand complicated things, I am very grateful.
⌨ (0:00:00) Intro
⌨ (0:00:58) Data/Colab Intro
⌨ (0:08:45) Intro to Machine Learning
⌨ (0:12:26) Features
⌨ (0:17:23) Classification/Regression
⌨ (0:19:57) Training Model
⌨ (0:30:57) Preparing Data
⌨ (0:44:43) K-Nearest Neighbors
⌨ (0:52:42) KNN Implementation
⌨ (1:08:43) Naive Bayes
⌨ (1:17:30) Naive Bayes Implementation
⌨ (1:19:22) Logistic Regression
⌨ (1:27:56) Log Regression Implementation
⌨ (1:29:13) Support Vector Machine
⌨ (1:37:54) SVM Implementation
⌨ (1:39:44) Neural Networks
⌨ (1:47:57) Tensorflow
⌨ (1:49:50) Classification NN using Tensorflow
⌨ (2:10:12) Linear Regression
⌨ (2:34:54) Lin Regression Implementation
⌨ (2:57:44) Lin Regression using a Neuron
⌨ (3:00:15) Regression NN using Tensorflow
⌨ (3:13:13) K-Means Clustering
⌨ (3:23:46) Principal Component Analysis
⌨ (3:33:54) K-Means and PCA Implementations
Thanks
Ihihhhih
Ihihhhih
how are these things useful?
i dont see how
just watch the whole video
I just finished taking this course. I took a while. but she explains the theory and examples. I loved the math part of it. she goes on explaining the Supervised vs Unsupervised ML tools and methods. I learnt a lot also with Pyrhon. Thanks for your effort.
I have to agree with those calling this tutorial too hard. I am a professional developer studying Cyber-Sec at the Master's level and found the first hour of the tutorial to be so intimidating that I had to go and learn Python again, just to boost my confidence. I followed it by getting a tutorial on Pandas as well as Numpy, those helped.
I came back and realized that, while this is a really good tutorial, it isn't beginner-friendly at all. The kind of stuff Kylie accomplishes in a single line needs multiple lines from me and many more minutes to understand what's going on.
As advice to all the newbies, don't be intimidated, try taking the Python basics, Pandas and Numpy courses before attempting this tutorial, perhaps watch the first hour to see what's required and come back.
Wao, thanks a lot. I will learn python until pro!
skill issue
@@josh6698 don't go hard on him! I personally have 0 experience of numpy or pandas but the logic she explains is very simple, remember you're here to learn ML concepts, you can revise python syntax later.
@@kklol07 I cannot understand her. Any direction where to take this course?
I cannot understand her. Any direction where to take this course?
Thanks for an amazingly simplified approach to ML 👍
If only everyone recognised the value of courses like this as you have done. First time I've seen that thanks/donation button which is sad but now will know how to contribute back! Thanks for letting me know that exists :D
give me some
this is perfect! By far the best I´ve found out there, such a clear and complete explanation. Great teacher.
Kylie is such a great teacher and obviously not only understands but applies these topics in the real world. What a great combination, thanks for the course!
what programming language she used in this course?
@yehiaelhariry9356 I'm pretty sure that she used python
@@yehiaelhariry9356 She is such a great teacher that she didn't mention that or why it is used.
@@leagueofotters2774 if you need an explanation on python then you are probably watching the wrong video lol
@@leagueofotters2774 Because it's obvious to anyone interested in machine learning that Python is the language you use. If you can't even recognise that the language she used in the video is Python, what are you doing watching a course on machine learning?
You are literally the best, I've been looking for a tutorial for three days and yours works
Absolutely brilliant. As mentioned in the intro Kylie is a true genius. god bless her
This is my first Course which I've completed from FCC, got a good understanding on ML now, Thank you !!
my 7th day - still not finished. Just so nice to see someone do ML work live! Thank you
r u a programmer before?
@@liudavid9792 jut dont have the time to see the whole 3 hours in one shot. I had to watch few minutes at a time. :)
@@liudavid9792 8the
@@geld5220 A
watch at 2x speed
Her voice and way of teaching is so soothing. I fell asleep listening to her and I am gonna watch this every night.
Semester video
This is the best course ever on youtube about machine learning . She know the fundamental and know what to teach . ❤
Thanks for the explanations! it is really detailed, her tone is comfortable, I can easily to understand what she said and she elaborates each steps (it is so important as every self learner can know the rationale behind each steps).
Hope I can watch another lesson videos of this editor!
Hey, invite you to use my AI learning tool -- Coursnap for this course! It has course outlines and course shorts, so you can get the gist of 1-hour in just 5 minutes. Try it out and boost your learning efficiency!
If you're getting an error about comparing a list to a float. Changing the "least_val_loss" variable to a list with two infinite floats will fix it. Like this: least_val_loss = [float('inf'), float('inf')]
thank you so much, that really drove me crazy. can you explain why it fixed it, and why she didn't need to do it in the video?
@@sigmaohiofortniterizz That section had the correction edited out. Right after she says "now we wait" (2:05:50) the video skips her debugging and cuts straight to the correct code. Notice the [0] that gets added to the "val_loss = model.evaluate(X_valid, Y_valid)" line right after the "now we wait".
when compling the model we asked for metrics, binary_crossentropy and accuracy, nn_model.compile(optimizer=tf.keras.optimizers.Adam(lr), loss='binary_crossentropy', metrics=['accuracy']), so val_loss = model.evaluate(X_valid, y_valid) returns a list with those two datapoints val_loss[loss, accuracy] we are only interested in loss to plot so val_loss[0] is needed.
@@michaelwindon681 can we add only one float to the list " least_val_loss = [float('inf')]" ? Does this do the same function as above
That‘’s the error I have encountered. Thank you so much!
I cannot even begin to describe how useful this video was for me. Endless thanks for the priceless knowledge Kylie and freeCodeCamp provided. This video seriously gave me an edge when it came to machine learning. It organized all the abstract concepts I have been hearing about while also describing them perfectly. I have learned SO much. Thank you, thank you, thank you!
This is amazing. I'm a data analyst and had some formal training in machine learning, but my classes were really surface and "teach yourself" style. This is so much better. I also find it easier to listen to women, so that's a bonus lol
Thanks . I am an experienced java lead . I always wanted to learn Machine learning
You guys are awesome.... just search for any tech skill on youtube and bang here is your great videos lectures or tutorials presents in results....
Such a great video. I’m already 30min and learning a lot. My major is centered around AI and machine learning so I wanted a sneak peek at what’s to come. Thank you!
I'm here for the same reason! About to go into junior year and leap headfirst into restricted electives
Absolutely amazing video, I have watched a lot of machine learning tutorials on youtube and this is by far the best one
Thank you so much for your brilliant tutorials and courses Kylie (please do more!!!)! Could you please recommend some books on the mathematics of machine learning (and books that you found useful when you dived into the subject).
I'm very excited about this. Her neural network video was amazing !
This is basically Machine learning for everyone who is a Python programmer. But seems to be a nice video. Will get back to this after I finish Python
Tysm for covering sooo much so quickly and it was all clear and to the point and I cant appreciate it enoughhhhhh!!!!!
It really worked for me after I look and try some tutorials, yours is the one that worked. Owe you a lot.
I love it! I must say this is one of the most comprehensive and well structured videos I've watched lately! Big kudos to Kylie!
Thank you a lot for this video. This is very interesting and informative. Keep posting like those amazing videos, this is awesome.
This worked incredibly well! I can finally play it thanks
A great introduction into machine learning, even for someone who may not be much familiar with Machine Learning, can learn a lot.
Enjoy these thorough, clear, visual explanations. She makes what we do accessible to beginners and a perfect refresh for seasoned users.
I’ll finish this course this time around hopefully 🙏🏾
Super useful video tbh. Once she's started going off with the coding it was amazing . Hope to get to that level one day
I really like the presentation of your video the time tou take to explain and build the concepts is spot on to get from zero knowledge to yes i understand this.
She is so good at this I woke up yesterday and startng writing an entirely custom Vector Matrix AI learning algorithm from scratch . After watching her explain the concepts from the simplest to the more complex. As a software programmer this has been very interesting to see how simple these algorithms really are.That is how you know you're a good teacher when im not even paying attention and somehow it sticks😁
@Freecodecamp you're always teaching us great skills with great mentors. Salute sir 🤗. Kindly upload the electric vehicle course if you have a great mentor available.
Kylie, thanks so much for your time. It was a pleasure learning from you.
She is so smart and makes things simple to understand👍
Hats off.. cannot imagine that it is possible to explain these concepts in such a simple way. Thank you and please keep on posting such learning videos.
This one is great video regarding to the Data Science and Machine Learning.
Woow, this course is amazing. It's presented in a such approachable and undestandable way. Thank you for creating this content.
The best teacher I have ever seen in this subject...!
It would be very beneficial for beginners to make Shallow, Deep and Convolutional Neural Networks from scratch. Because by doing so, they can learn many activations and their derivatives, forward propagation, and backward propagation. Along with, the dimensions of matrices and what is actually happening at each layer. Later on, they can shift to Tensorflow or Scikit when doing professional learning but I do advise to at least implement a neural network from scratch.
Have you Built NN from scratch?
@@lakshman587 Yes. A Shallow Neural Network. This week I will be making a Deep Neural Network Vectorized Approach from scratch and 2 weeks later I will also implement CNN from scratch
@@muhammaddarab7474 Can you share your code?
I have tried to hardcode the backpropagation algorithm for 1 input and 1 output with only 1 hidden layer
So even I need to implement full NN from scratch...
@@lakshman587 I can share my Shallow Neural Network code with you for 1 layer but for N Hidden layer (Deep Neural Network), you will have to wait till I implement it
@@muhammaddarab7474 Ok
I guess we are on same track.
Ill try to implement NN for N hidden layers from scratch...
Let's hope we will finish and gain knowledge on how NN works 🥲
All the best!
Yes, as everyone is the audience let's start with an example that few can relate to and then just jump into code, again, everyone is well versed in code, without any explanation or overview.
What do you mean, she started off with code, but quickly went on to explain what was going on. Besides, if you want to learn ML, you should be familiar with basic python and linear algebra.
that's a losing mindset. I'd save the video and come back to it once I understood the basics used
Prefect course for newbie. Teacher explains very good. 🎉
THANK YOU for making her course available on this channel !!!!!!! 👍😃👍
Thanks for making the excellent course!
Kylie explains better than my professors LOL. Great job FCC and Kylie, thank you very much
i like to see what your prof thinks when they know about this lol
The best course ever. THANK YOU!!!!
41:52
1:02:49
Just reminding myself where I stopped the video. Absolutely fantastic walkthrough!
This is a great course and thank you so much. I was able to understand and learn so many new things! Some topics I had struggled a bit were quite clear through this course.
what programming language she used in this course?
@@yehiaelhariry9356 Python.
@@yehiaelhariry9356 bruh😂😂😂🤣🤣🤣
18:10 As soon as you mentioned "Hot Dog or Not Hot Dog", it instantly reminded me of Jian Yang's classification model from the HBO comedy series Silicon Valley. 😂
But this course is very useful and easy to grasp for beginners like us. 👍👍
Same here bro. Jian Yang is a legend
love this XDD
Not Hotdog
I have no idea in programming but understood little bit. It's exciting 😊
Kylie and this course are f**ing AWESOME!!! So clear explained!! Thank you so much for sharing!
A great introduction into machine learning, even as someone who is into the tech industry, I am not too familiar with machine learning.
Repost from deep in the comments:
⌨ (0:00:00) Intro
⌨ (0:00:58) Data/Colab Intro
⌨ (0:08:45) Intro to Machine Learning
⌨ (0:12:26) Features
⌨ (0:17:23) Classification/Regression
⌨ (0:19:57) Training Model
⌨ (0:30:57) Preparing Data
⌨ (0:44:43) K-Nearest Neighbors
⌨ (0:52:42) KNN Implementation
⌨ (1:08:43) Naive Bayes
⌨ (1:17:30) Naive Bayes Implementation
⌨ (1:19:22) Logistic Regression
⌨ (1:27:56) Log Regression Implementation
⌨ (1:29:13) Support Vector Machine
⌨ (1:37:54) SVM Implementation
⌨ (1:39:44) Neural Networks
⌨ (1:47:57) Tensorflow
⌨ (1:49:50) Classification NN using Tensorflow
⌨ (2:10:12) Linear Regression
⌨ (2:34:54) Lin Regression Implementation
⌨ (2:57:44) Lin Regression using a Neuron
⌨ (3:00:15) Regression NN using Tensorflow
⌨ (3:13:13) K-Means Clustering
⌨ (3:23:46) Principal Component Analysis
⌨ (3:33:54) K-Means and PCA Implementations
She's excellent.I wish she do deep learning tutorials
She teaches so good. Not to forget that fast typing ASMR music to my ears 🤩
The best introductory machine learning video!
I have tremendous respect for Kylie.
I think it is the clearest and easiest to understand explanation in the world.
Thank you so much.
Simple and straightforward content, helped me make the decision to buy
Loving this course. Very Intuitive and practical.
It's 2:16 am and I just woke up to this on my screen and yes I wanted it so badly. So strange but it's now downloaded and will watch and learn when I wake up. Thank you
Great course, excellent combination of material with application examples.
Thanks for sharing such an amazing content!!
It would be helpful for people without coding experience to explain the libraries being used, the languages, and how it works. Or to explain the bare minimums required. This seems more like it's geared to people with python programming experience as a base
This was definitely valuable! Thanks for sharing Adnan!!!
"Please feel free to correct me in the comments" your resilience must be nothing short of your intelligence. Mad props.
🎯Course outline for quick navigation:
[00:00-06:57]1. Machine learning and data analysis
-[00:00-00:28]Kylie ying, a physicist and engineer, will teach machine learning to beginners.
-[01:05-02:12]Uci ml repository offers magic gamma telescope dataset for predicting particle type using camera patterns.
-[05:04-05:38]Using 'fdist' and 'class' to label columns in a csv file to a pandas data frame.
-[05:59-06:28]Converting gs to 0s and hs to 1s in data frame class.unique.
[07:01-34:39]2. Machine learning fundamentals
-[08:00-08:29]In supervised learning, 10 features are used to predict the class label g.
-[09:53-10:27]Supervised learning uses labeled inputs to train models and learn outputs.
-[13:14-13:45]Two categories: qualitative and categorical data.
-[23:57-24:24]Data set is divided into training, validation, and testing sets, with distribution like 60%, 20%, and 20% or 80%, 10%, and 10% depending on statistics.
[34:39-44:02]3. Data scaling, splitting, and oversampling for machine learning
-[35:13-35:43]Splitting data into train, valid, and test sets, shuffling with 60% split
-[37:38-38:10]Imported standard scaler from sklearn for data scaling.
-[39:06-39:36]Using numpy to reshape 1d vector y into 2d object for stacking x and y.
-[40:28-40:59]Around 7,000 gammas and 4,000 hadrons, oversampling needed.
-[42:05-42:37]Oversample the smaller class to match the larger class in the dataset.
-[43:43-44:12]Validation and test sets are not oversampled to assess model performance on unlabeled data.
[44:02-57:30]4. K nearest neighbors model and its implementation
-[44:35-45:03]Introducing knn model for predicting family size based on income.
-[49:02-49:34]Using k-nearest neighbors algorithm with a k of 3 or 5 to determine labels for data points.
-[52:37-53:10]Introduction to k-nearest neighbors and using sklearn for implementation.
-[56:39-57:12]Precision score: 77-84%, recall: 68-89%
[57:31-01:36:19]5. Probabilistic classification models
-[57:31-58:09]Analyzing unbalanced test data, improving f1 score to 0.87, accuracy at 82%
-[01:01:35-01:02:06]Probability of having covid, given a positive test, is 96.4%.
-[01:06:00-01:06:32]Probability of positive test given disease is 0.99, probability of having disease is 0.1.
-[01:12:22-01:12:52]Naive bayes assumes independence in calculating joint probability.
-[01:16:45-01:17:15]Using the training set, we apply the map principle to maximize expression for hypothesis selection.
-[01:26:27-01:27:43]Logistic regression fits data to sigmoid function to build a model with multiple features.
-[01:28:56-01:29:33]Model achieves 65% precision, 71% recall, and 77% accuracy, outperforming naive bayes but not knn.
-[01:33:30-01:33:58]Goal: maximize margins in svms for best class separation.
[01:36:21-01:46:45]6. Svms and neural networks
-[01:37:13-01:37:41]Exploring the definition and power of svms through the kernel trick.
-[01:38:23-01:39:17]Achieved 87% accuracy with svm model, class one scores 0.9, covered four classification models: svm, logistic regression, naive bayes, and knn.
-[01:39:44-01:40:12]Neural networks consist of input, hidden, and output layers, each containing neurons.
-[01:42:50-01:43:22]Introduction of sigmoid, tanh, and relu activation functions to prevent collapse of the model into a linear one.
-[01:43:42-01:44:16]During training, the model adjusts based on l2 loss function to reduce error.
-[01:45:53-01:46:26]Updating weight w0 with a new value using a factor alpha.
[01:46:45-02:03:23]7. Neural network training
-[01:46:45-01:47:13]Explaining the use of learning rate and negative gradient in adjusting neural net convergence.
-[01:47:39-01:48:13]Iteratively adjust weights in neural network for machine learning.
-[01:55:58-01:56:24]Validation accuracy improved from 0.77 to around 0.81, and loss is decreasing, indicating positive progress.
-[01:56:50-01:57:27]Discussing grid search for hyperparameters with variations like 64 nodes and 16 nodes, and adjusting learning rate and epochs.
-[02:00:57-02:01:36]Analyzing model performance with loss and accuracy plots.
[02:03:24-02:36:19]8. Model training, evaluation, and regression in ml
-[02:07:02-02:07:37]Best performance achieved with 64 nodes, 0.2 dropout, 0.001 learning rate, and batch size of 64.
-[02:16:09-02:16:46]Using linear regression to minimize error and make predictions for data points.
-[02:17:19-02:17:51]Kylie discusses assumptions, including linearity in data analysis.
-[02:28:04-02:28:32]The root mean squared error allows expressing error in dollars and same unit.
-[02:33:09-02:33:32]High r squared indicates good prediction, adjusted r squared accounts for terms.
[02:36:20-03:12:03]9. Data analysis and regression modeling
-[02:42:59-02:43:25]Data frame modified: wind, visibility, and functional dropped, leaving 6 columns.
-[02:48:10-02:48:42]Demonstrating simple linear regression using temperature data.
-[02:54:17-02:55:15]Improved r squared from 0.4 to 0.52 indicates progress in regression analysis using tensorflow.
-[03:11:18-03:11:45]Neural net underestimates for larger values, linear regressor also used.
[03:12:04-03:22:13]10. Unsupervised learning: k-means clustering
-[03:12:04-03:12:32]Linear regressor is limited in capturing non-linear relationships; suggesting the need for alternative models in certain cases.
-[03:12:58-03:13:29]Unsupervised learning: using k-means clustering to compute k clusters from unlabeled data.
-[03:17:35-03:18:02]Data points recalculated to update centroids for groups.
-[03:21:54-03:22:33]Using expectation maximization, reaching stable clusters allows stopping iteration.
[03:22:15-03:33:52]11. Expectation maximization and principal component analysis
-[03:22:15-03:23:20]Using expectation maximization for unsupervised learning to find data patterns and structure.
-[03:24:10-03:24:42]Pca reduces multi-dimensional data to one dimension to capture key information.
-[03:25:49-03:26:19]Demonstrating distance using pca to find direction in space.
-[03:30:55-03:31:23]Minimizing projection residual to find largest variance dimension in pca.
[03:33:53-03:53:51]12. Unsupervised learning and dimensionality reduction
-[03:33:53-03:34:22]Implementing unsupervised learning on the uci seeds dataset with three types of wheat kernels
-[03:35:06-03:35:51]Importing pandas and seedborne for specific class.
-[03:45:46-03:46:12]Using k-means, identified 3 classes based on compactness and asymmetry in scatter plot.
-[03:47:54-03:48:18]Pca reduces multiple dimensions into a lower dimension number.
-[03:51:32-03:53:49]Unsupervised learning algorithm predicts three categories fairly well without labels.
-[03:53:11-03:53:37]Algorithm predicts three categories fairly well without labels. demonstrates unsupervised learning.
offered by Coursnap
Hi, thank you very much for this high quality course, a real master piece. Maybe as a newbie, I could be wrong but it seems there is an error in the explaination of the data preprocessing (usage of standardscaler). The standard scaler has to be fit and transform the train sample however, the scaler parameters should then be stored and it has to ONLY transform the next sample (valid and test). Fitting the scaler on the valid and test sample could have a bad effect the the X values and misaligned the data from the train sample. Once again, I'm new on this topic but it seems quite clear this standard scaler usage is inappropriate.
Eu amo esse tipo de vídeo! até me inscrevi.
GREAT Course! Wish we could have more videos from HER!!
Did a refresher on ML as it's been a while... Kylie rocks! Delivered with the right amount of theory combined with examples and importantly, clarity! Top marks!!!!!
Thanks for the content. This isn't for everyone though.. She speeds through so much and beginners are just sitting here going "What the hell is going on...". Looks like that was being pointed out early in the comments and then there seems to be a flood "this is so clear and fully fleshed out!" comments that have been pushed to the top.. That's a bit sus.
no u actually just have needed to know programming and some basic math. This is much better than that 9hr one by that indian kid
I was so excited to see this course because usually freecodecamp courses are for beginners but this one is not. You need to have good knowledge of numpy and other libraries to be able to learn more from this course. Really disappointing. They should let users know the basics that are required for this course. But ultimately this is a very good course and it's free so really appreciate it guys. Thanks
I think most people understand intuitively that something as complex sounding as “machine learning” will require prior knowledge. Lol. It’s like walking into an “Intro to Quantum Physics and Relativity” class and expecting to get by without understanding Newtonian physics or calculus.
I would never complain about free education - do one of their Python/numpy courses and return here. Better to build on what you know already than every course starting from 0 :)
Thanks for creating this awesome tutorial for free ❤
her explanation extremely helpful for my Diabetes Machine Learning Research
Great video...stayed up all evening watching; wondering if you would demonstrate ketosis distribution measures or is that too difficult to script out?
Is there a summarised version of this lesson? perhaps for someone that isn't intending to gain a fullon knowledge but gain a sufficient, generalized idea of every subject Kylie is covering :),
Based on what I’ve looked into regarding this subject, this IS the summarized version.
More ? are you sure ?
@@eltrylogis haha yeah man dale
Thank you so much for this amazing video! I had to take a nap halfway through because there was so much to digest haha
I think it is a very advanced video honestly. it should mention that you need a very solid foundation of statistics, coding, data science to even understand what is going on!!! its a medium level video
Thanks for this great tutorial freecodecamp. We also need statistical data analysis with Python and/or R (especially descriptive statistics, probability distributions and hypothesis tests , anova etc... )
Did you find this anywhere something useful
Great course! Thank you for the hard work!
Estou muito satisfeito por ter me inscrito e ter acesso a este conteúdo maravilhoso.
thank you FCC and kylie ying for this amazing ML course....best for recap in short time about classical ML domain
Can someone confirm if this goes beyond the basics and actually helps you to build stuff? I’ve done plenty of these courses and most of them only contain basic information about a 100 different things and very little demonstration of actually building a significant project from raw data. So, my fundamentals should be fine after a bit of a brush-up but I need a course that goes beyond.
read the title once again
It is just basic not in deep knowledge
You are in what we call tutorial hell
Try to build something on your own it will help
The best way to learn is to practice. You can find free datasets online and mess with them to practice skills.
train,valid,test=np.split(df.sample(frac=1),[int(0.6*len(df)),int(0.8*len(df))])
key error 0??
i am getting the same error, how did u rectify it?
I have refused to think about this for a long time. I will now think about it.
I didn't realize it was a natural product! Now i'm gonna try without worries :)
You hop over a few items that might have benefited from a couple seconds of explanation for non-mathy newbies (e.g. p vs p/(1-p) in the logistical regression portion) but otherwise excellent!
There's no way you're putting out all of this for free. Better explained than in school ong
Could you explain what the things are that you are labeling and importing as you are doing them?
It helps me to make connections in my brain if I can associate what you are doing with the reason behind it. Otherwise it is just an exercise in memorizing and just replicating what you are doing without any understanding.
Thank you for sharing and all the effort put into it.
Amazing course, I coded it along in VSCode. This made me learn quite a lot about conda, environments, etc. With this kind of hands on learning, not only I believe I can start doing data analysis and creating my own models and understanding their predictions. Thank you all so much! 👏Great work! (And well, it's not for Python noobies... Anyone past the basics with most of the libraries used can understand it)
Awesome!!
Hey, thanks for sharing. Great video. I get an error at this code: train, X_train, y_train = scale_dataset(train, oversample = True)
ValueError: input contains NaN, infinity or a value too large for dtype ('float64')
Does anyone else face this issue /know how to solve it?
It might be a problem with your dataset, and not the code. Try doing some basic pre-processing on it. It might be a missing value, or a large number somewhere. Good luck!
English is not my native language and I am new at machine learning but it was easy for me to understand you. i watched too many videos about ML but this is the best
Nice!